Unlocking Unconventional Production Optimization Opportunities Using Reduced Physics Models for Well Performance Analysis – Case Study

S. Sankaran, Diego Molinari, Hardikkumar Zalavadia, T. Stoddard, Wenyue Sun, Gagan Singh, Chris James
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Abstract

Economic pressure to improve production efficiency in unconventional reservoirs has met a stiff challenge to scale up traditional reservoir modeling methods to the entire field for quantifying well performance. The main reasons are lack of availability of key reservoir and well parameters and difficulty to setup and maintain models because of the large well count and rapid pace of operations. As a result, decline curve analysis is still the prevailing method for large scale evaluations, which does not consider routine pressure variations and operational constraints. Analytical rate transient (RTA) models warrant identification of flow regimes and geometrical assumptions (well and fractures) to apply discrete analytical models for various flow segments. This inherent limitation of RTA makes it interpretive and not conducive to fieldscale application, besides often lacking necessary inputs for all wells. It is desirable to have better understanding through a robust and consistent well performance analysis method at field scale to unlock significant production optimization opportunities with existing field infrastructure and investment. We have applied a reduced physics formulation based on Dynamic Drainage Volume (DDV) using commonly measured data for most wells (namely, flowback data, daily production rates, and wellhead pressure) to calculate continuous pressure depletion, transient productivity index (PI) and inflow performance relationship (IPR). This transient well performance (TWP) method eliminates the surface and wellbore operational impacts to extract the true reservoir signal that can be used for robust well performance analysis and forecasting. We applied the TWP method in multiple basins with large well counts (more than 1000 wells) producing under a variety of methods. In this paper, we present several case studies illustrating various production optimization opportunities, focusing on naturally flowing and gas-lifted wells. The fluid properties and bottomhole pressure estimated using data-driven methods for all wells provided excellent match with blind data (PVT lab reports and downhole gauge data). The TWP method normalizes reservoir and completion quality to extract valuable insights on effectiveness of well and completions design in the presence of varying geological and fluid properties. The transient PI and dynamic IPR results provided valuable insights on how and when to select various artificial lift systems. During gas lift, we identified several wells that were over-injecting gas volumes at higher compressor discharge head, with line of sight to significant operational cost savings and reduced energy consumption. The proposed methodology combines pragmatic use of physics and data-driven methods to solve a critical need for analyzing unconventional reservoirs. Field application of the novel DDV method on large well population has been quite successful in identifying various optimization opportunities that would not have been possible, timely, or repeatable with other traditional methods.
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利用简化物理模型进行油井动态分析,解锁非常规生产优化机会-案例研究
提高非常规油藏生产效率的经济压力,给将传统油藏建模方法推广到整个油田以量化油井动态带来了严峻挑战。主要原因是缺乏关键油藏和井参数的可用性,以及由于大量井数和快速的作业节奏而难以建立和维护模型。因此,递减曲线分析仍然是大规模评价的主流方法,它没有考虑常规的压力变化和操作限制。分析速率暂态(RTA)模型保证了流动形式和几何假设(井和裂缝)的识别,以便将离散分析模型应用于不同的流动段。RTA固有的局限性使其具有解释性,不利于现场规模的应用,而且通常缺乏对所有井的必要输入。在油田规模上,通过稳健、一致的油井动态分析方法,可以更好地理解,从而利用现有的油田基础设施和投资,获得重大的生产优化机会。我们采用了基于动态排量(DDV)的简化物理公式,使用大多数井的常用测量数据(即反排数据、日产量和井口压力)来计算连续压力耗尽、瞬态产能指数(PI)和流入动态关系(IPR)。这种瞬态井动态(TWP)方法消除了地面和井筒操作的影响,提取了真实的油藏信号,可用于稳健的井动态分析和预测。我们将TWP方法应用于多个大井数(超过1000口井)的盆地,这些盆地采用了多种方法进行生产。在本文中,我们介绍了几个案例研究,说明了各种生产优化机会,重点是自然流动和气举井。采用数据驱动方法估算的所有井的流体性质和井底压力与盲测数据(PVT实验室报告和井下测量数据)吻合良好。TWP方法规范了储层和完井质量,在不同地质和流体性质的情况下,对井和完井设计的有效性进行了有价值的分析。瞬态PI和动态IPR结果为如何以及何时选择各种人工举升系统提供了有价值的见解。在气举过程中,我们发现有几口井在压缩机排放水头较高的情况下过量注入气体,从而显著节省了运营成本,降低了能耗。所提出的方法结合了物理和数据驱动方法的实用应用,以解决分析非常规油藏的关键需求。新型DDV方法在大井群的现场应用非常成功,能够识别出各种优化机会,而这些优化机会是其他传统方法无法及时实现或无法重复的。
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